2.2 活跃原告在地区上的数量分布（包含整体，年份变化，以及其地区垄断的分析）

	year	province	plaintiff_count	case_count	success_count	success_rate
0	2014	上海市	4	20	17.0	0.850000
1	2014	北京市	6	21	21.0	1.000000
2	2014	天津市	1	1	1.0	1.000000
3	2014	安徽省	2	10	10.0	1.000000
4	2014	广东省	8	39	37.0	0.948718

活跃原告数量-时间省份 地区信息图

'\n1. 变化不大\n2. 由少到多\n3. 由多到少\n'

	year	province	original_plaintiff_count	original_case_count	original_success_count
0	2014	上海市	23	61	53.0
1	2014	云南省	3	4	4.0
2	2014	内蒙古自治区	1	1	1.0
3	2014	北京市	35	62	58.0
4	2014	吉林省	5	10	0.0

	year	province	original_plaintiff_count	plaintiff_count	original_case_count	case_count	original_success_count	success_count	monopolistic_plaintiff
0	2014	上海市	23	4.0	61	20.0	53.0	17.0	0.327869
1	2014	云南省	3	0.0	4	0.0	4.0	0.0	0.000000
2	2014	内蒙古自治区	1	0.0	1	0.0	1.0	0.0	0.000000
3	2014	北京市	35	6.0	62	21.0	58.0	21.0	0.338710
4	2014	吉林省	5	0.0	10	0.0	0.0	0.0	0.000000

2.3 活跃原告的活跃年份统计

	plaintiff	year	active_years	min_year	max_year
28	周开礼	[2016, 2017, 2018, 2019, 2020, 2014, 2015]	7	2014	2020

	min_year	active_years	plaintiff
0	2014	3	2
1	2014	4	8
2	2014	5	9
3	2014	6	12
4	2014	7	1

看活跃年份与赢率和金额的关系

<ipython-input-35-47792c016577>:3: FutureWarning:

The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead

2.8 活跃原告是否倾向于在自己赢过的法院发起诉讼

（赢率已修改完成）

                            OLS Regression Results                            
==============================================================================
Dep. Variable:             case_count   R-squared:                       0.768
Model:                            OLS   Adj. R-squared:                  0.768
Method:                 Least Squares   F-statistic:                     4189.
Date:                Thu, 04 Nov 2021   Prob (F-statistic):               0.00
Time:                        09:59:45   Log-Likelihood:                -4989.3
No. Observations:                1268   AIC:                             9983.
Df Residuals:                    1266   BIC:                             9993.
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
=================================================================================
                    coef    std err          t      P>|t|      [0.025      0.975]
---------------------------------------------------------------------------------
Intercept         1.3756      0.389      3.534      0.000       0.612       2.139
success_count     1.0284      0.016     64.722      0.000       0.997       1.060
==============================================================================
Omnibus:                     3000.196   Durbin-Watson:                   1.947
Prob(Omnibus):                  0.000   Jarque-Bera (JB):         20939076.872
Skew:                          22.420   Prob(JB):                         0.00
Kurtosis:                     630.943   Cond. No.                         27.4
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

2.9 如果在某一法院赢率很低，是否案件数量也不多

活跃原告在某一法院的退出不受其输掉的影响

<ipython-input-39-f0741ef819de>:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

1084 143 0.13388734995383195

2728 360 0.13107511045655376

0.8899788118967276

2.10 统计活跃原告告的被告是否有重复(同一个原告告过哪些被告，同一个被告被哪些原告共同告过)

与activePlaintiffPatterns1的2.7有重复

[NbConvertApp] Converting notebook activePlaintiffPatterns2.ipynb to html